{"id":"W2293258973","doi":"10.18293/seke2015-249","title":"Embedded Real Time Blink Detection System for Driver Fatigue Monitoring","year":2015,"lang":"en","type":"article","venue":"Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering","topic":"Sleep and Work-Related Fatigue","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Alberta Innovates; University of Calgary","keywords":"Computer science; Latency (audio); Histogram; Artificial intelligence; Task (project management); Response time; Computer vision; Real-time computing; Engineering; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004883296,0.0004084296,0.0003770633,0.0003860939,0.0001069846,0.0001547732,0.0006041116,0.0002874857,0.00001392503],"category_scores_gemma":[0.0006036314,0.0003689012,0.0001515419,0.0003666559,0.00004163972,0.0002909506,0.0001612708,0.0004491733,0.00002692856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002732225,"about_ca_system_score_gemma":0.00003775217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001364717,"about_ca_topic_score_gemma":3.457341e-7,"domain_scores_codex":[0.998203,0.000002732634,0.0004883884,0.0005104049,0.0003671396,0.0004283507],"domain_scores_gemma":[0.9982264,0.0001206729,0.000223855,0.0001091909,0.001108898,0.000210963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0011134,0.0005961351,0.02946277,0.001663261,0.002726464,0.000006286698,0.02770817,0.005099401,0.519347,0.3772999,0.00555665,0.02942057],"study_design_scores_gemma":[0.01561866,0.002251092,0.02275647,0.02136774,0.001098439,0.0003106832,0.01866246,0.3192504,0.5876958,0.0004927822,0.005177447,0.005317985],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9793953,0.0001841354,0.002324247,0.0000704856,0.005161528,0.0005816627,0.00002301488,0.0009017364,0.01135791],"genre_scores_gemma":[0.9937992,0.00001339058,0.004569625,0.000003841823,0.0008070206,0.0001996042,0.00000376271,0.00008961123,0.0005139395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3768071,"threshold_uncertainty_score":0.9998763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04086291848435231,"score_gpt":0.2830649568662733,"score_spread":0.242202038381921,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}